Intuit Inc Patent Portfolio Statistics

Intuit Inc.

Profile Summary

This article summarizes the perfomance of the assignee in the recent years. The overall statistics for this portfolio help to analyze the areas where the assignee is performing well. The filing trend, perfomance across the tech centers and the perfomance of the recent applications has been mentioned below. All the stats are calculated based on the perfomance in USPTO.

How does the overall patent portfolio of Intuit Inc. look like?

Total Applications: 1,793
Granted Patents: 1,360
Grant Index 86.96 %
Abandoned/Rejected Applications: 204 (13.04%)
In-Process Applications: 208
Average Grant Time: 3.51 Years
Average Office Actions: 2.19

Which Technology Area Intuit Inc. is filing most patents in? (Last 10 years)

Art Unit Definition Total Applications
3687 Business Methods 91
3627 Business Methods – Incentive Programs, Coupons; Operations Research; Electronic Shopping; Health Care; Point of Sale, Inventory, Accounting; Cost/Price, Reservations, Shipping and Transportation; Business Processing 84
3694 Business Methods – Finance/Banking/ Insurance 70
Opap Parked GAU 69
3691 Business Methods – Finance/Banking/ Insurance 62

How many patents are Intuit Inc. filing every year?

Year Total Applications
2022 0*
2021 32*
2020 149
2019 134
2018 140

*The drop in the number of applications filed in last two years compared to previous years is because applications can take up to 18 months to get published

Recently filed patent applications of Intuit Inc. in USPTO?

Publication number: US20220027745A1
Application number: 17/495,707

The present disclosure relates to processing support data to increase a self-support knowledge base. In some embodiments, assisted support data is received comprising a record of an interaction between a user and a support professional. In certain embodiments, a support data set is extracted from the assisted support data. In some embodiments, feedback related to the support data set is received. The feedback may include an indication that the support data set is ready to be included in the self-support knowledge base. In some embodiments, upon determining, based on the feedback, that the support data set is ready to be used for self-support, the support data set is added to the self-support knowledge base. The self-support knowledge base may be accessible by a plurality of users.

Publication date: 2022-01-27
Applicant: Intuit Inc.
Inventors: Cannon Matthew

Publication number: US20220027570A1
Application number: 17/495,681

Certain aspects of the present disclosure provide techniques for generating a replacement sentence with the same or similar meaning but a different sentiment than an input sentence. The method generally includes receiving a request for a replacement sentence and iteratively determining a next word of the replacement sentence word-by-word based on an input sentence. Iteratively determining the next word generally includes evaluating a set of words of the input sentence using a language model configured to output candidate sentences and evaluating the candidate sentences using a sentiment model configured to output sentiment scores for the candidates sentences. Iteratively determining the next word further includes calculating convex combinations for the candidate sentences and selecting an ending word of one of the candidate sentences as the next word of the replacement sentence. The method further includes transmitting the replacement sentence in response to the request for the replacement sentence.

Publication date: 2022-01-27
Applicant: Intuit Inc.
Inventors: Nicholas Roberts

Publication number: US20220030087A1
Application number: 17/495,664

Techniques are disclosed to predict experience degradation in a microservice-based application comprising a plurality of microservices. Quality of service metrics are derived for each node from the historical event log data of nodes forming a plurality of directed acyclic graph (DAG) paths in the multiple-layer nodes. A clustering model clusters the plurality of quality of service metrics according to multiple levels of quality of experience and determines respective value ranges of each quality of service metric for the multiple levels of quality of experience. Each quality of service metric is labeled with one of the multiple levels of quality of service according to the respective value ranges. A support vector machine model predicts various experience degradation events which are expected to occur during the operation of the microservice-based application.

Publication date: 2022-01-27
Applicant: Intuit Inc.
Inventors: Chatterjee Shreeshankar

How are Intuit Inc.’s applications performing in USPTO?

Application Number Title Status Art Unit Examiner
17/495,707 Processing And Re-Using Assisted Support Data To Increase A Self-Support Knowledge Base OPAP Central, Docket
17/495,681 Generating Replacement Sentences For A Particular Sentiment OPAP Central, Docket
17/495,664 Method And Apparatus For Predicting Experience Degradation Events In Microservice-Based Applications OPAP Central, Docket
17/493,674 Supervised Machine Learning Algorithm Application For Image Cropping And Skew Rectification OPAP Central, Docket
17/449,050 Semantic And Standard User Interface (Ui) Interoperability In Dynamically Generated Cross-Platform Applications OPAP Central, Docket